{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, absolute_import, division, unicode_literals, with_statement\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "import numpy as np\n",
    "from cleanlab.classification import LearningWithNoisyLabels\n",
    "from cleanlab.noise_generation import generate_noisy_labels\n",
    "from cleanlab.util import value_counts\n",
    "from cleanlab.latent_algebra import compute_inv_noise_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `cleanlab` can be used with any classifier and dataset for multiclass learning with noisy labels. Its comprised of components from the theory and algorithsm of **confident learning**. It's a Python class that wraps around any classifier as long as .fit(X, y, sample_weight), .predict(X), .predict_proba(X) are defined. Inspect the **cleanlab** package for documentation.\n",
    "\n",
    "## Here we show the performance with LogisiticRegression classifier versus LogisticRegression without any help from confident learning on the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seed for reproducibility\n",
    "seed = 2\n",
    "clf = LogisticRegression(solver = 'lbfgs', multi_class = 'auto', max_iter = 1000)\n",
    "rp = LearningWithNoisyLabels(clf = clf, seed = seed)\n",
    "np.random.seed(seed = seed)\n",
    "\n",
    "# Get iris dataset\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data  # we only take the first two features.\n",
    "y = iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    from matplotlib import pyplot as plt\n",
    "    _ = plt.figure(figsize=(12,8))\n",
    "    color_list = plt.cm.tab10(np.linspace(0, 1, 6))\n",
    "    _ = plt.scatter(X_train[:,1], X_train[:,3], color = [color_list[z] for z in y_train], s = 50)  \n",
    "    ax = plt.gca()\n",
    "#     plt.axis('off')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    _ = ax.get_xaxis().set_ticks([])\n",
    "    _ = ax.get_yaxis().set_ticks([])\n",
    "    _ = plt.title(\"Iris dataset (feature 3 vs feature 1)\", fontsize=30)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    print(\"Plotting is only supported in an iPython interface.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate lots of noise.\n",
    "noise_matrix = np.array([\n",
    "    [0.5, 0.0, 0.0],\n",
    "    [0.5, 1.0, 0.5],\n",
    "    [0.0, 0.0, 0.5],\n",
    "])\n",
    "\n",
    "py = value_counts(y_train)\n",
    "# Create noisy labels\n",
    "s = generate_noisy_labels(y_train, noise_matrix)\n",
    "\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    from matplotlib import pyplot as plt\n",
    "    _ = plt.figure(figsize=(15,8))\n",
    "    color_list = plt.cm.tab10(np.linspace(0, 1, 6))\n",
    "    for k in range(len(np.unique(y_train))):\n",
    "        X_k = X_train[y_train == k] # data for class k\n",
    "        _ = plt.scatter(X_k[:,1], X_k[:,3], color=[color_list[noisy_label] for noisy_label in s[y_train==k]], s=200, marker=r\"${a}$\".format(a=str(k)), linewidth=1)\n",
    "#     plt.scatter(X_train[:,1], X_train[:,3], color = [color_list[z] for z in s], s = 50, marker=[r\"${a}$\".format(a=str(w)) for w in y_train]) \n",
    "    _ = plt.scatter(\n",
    "        X_train[s != y_train][:,1], \n",
    "        X_train[s != y_train][:,3], \n",
    "        color = [color_list[z] for z in s], \n",
    "        s = 400, \n",
    "        facecolors='none', \n",
    "        edgecolors='black', \n",
    "        linewidth=2, \n",
    "        alpha = 0.5,\n",
    "    ) \n",
    "    ax = plt.gca()\n",
    "#     plt.axis('off')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    _ = ax.get_xaxis().set_ticks([])\n",
    "    _ = ax.get_yaxis().set_ticks([])\n",
    "    _ = plt.title(\"Iris dataset (features 3 and 1). Label errors circled.\", fontsize=30)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    print(\"Plotting is only supported in an iPython interface.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITHOUT confident learning, Iris dataset test accuracy: 0.6\n",
      "\n",
      "Now we show the improvement using confident learning to characterize the noise\n",
      "and learn on the data that is (with high confidence) labeled correctly.\n",
      "\n",
      "WITH confident learning (noise matrix given), Iris dataset test accuracy: 0.83\n",
      "WITH confident learning (noise / inverse noise matrix given), Iris dataset test accuracy: 0.83\n",
      "WITH confident learning (using latent noise matrix estimation), Iris dataset test accuracy: 0.83\n",
      "WITH confident learning (using calibrated confident joint), Iris dataset test accuracy: 0.83\n"
     ]
    }
   ],
   "source": [
    "print('WITHOUT confident learning,', end=\" \")\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "_ = clf.fit(X_train, s)\n",
    "pred = clf.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test), 2))\n",
    "\n",
    "print(\"\\nNow we show the improvement using confident learning to characterize the noise\")\n",
    "print(\"and learn on the data that is (with high confidence) labeled correctly.\")\n",
    "print()\n",
    "print('WITH confident learning (noise matrix given),', end=\" \")\n",
    "_ = rp.fit(X_train, s, noise_matrix=noise_matrix)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test),2))\n",
    "\n",
    "print('WITH confident learning (noise / inverse noise matrix given),', end=\" \")\n",
    "_ = rp.fit(X_train, s, noise_matrix = noise_matrix, inverse_noise_matrix=compute_inv_noise_matrix(py, noise_matrix))\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test),2))\n",
    "\n",
    "print('WITH confident learning (using latent noise matrix estimation),', end=\" \")\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "rp = LearningWithNoisyLabels(clf = clf, seed = seed)\n",
    "_ = rp.fit(X_train, s)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test),2))\n",
    "\n",
    "print('WITH confident learning (using calibrated confident joint),', end=\" \")\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "rp = LearningWithNoisyLabels(clf=clf, seed=seed)\n",
    "_ = rp.fit(X_train, s)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance of confident learning across varying settings. To learn more about the hyper-parameter settings, inspect ```cleanlab/pruning.py```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param settings: {'prune_method': 'prune_by_class', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.87 \n",
      "\n",
      "Param settings: {'prune_method': 'both', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.8 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_noise_rate', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.8 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_noise_rate', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.8 \n",
      "\n",
      "Param settings: {'prune_method': 'both', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.77 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_class', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.73 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"prune_method\": [\"prune_by_noise_rate\", \"prune_by_class\", \"both\"],\n",
    "    \"converge_latent_estimates\": [True, False],\n",
    "}\n",
    "\n",
    "# Fit LearningWithNoisyLabels across all parameter settings.\n",
    "params = ParameterGrid(param_grid)\n",
    "scores = []\n",
    "for param in params:\n",
    "    clf = LogisticRegression(solver = 'lbfgs', multi_class = 'auto', max_iter = 1000)\n",
    "    rp = LearningWithNoisyLabels(clf = clf, **param)\n",
    "    _ = rp.fit(X_train, s) # s is the noisy y_train labels\n",
    "    scores.append(accuracy_score(rp.predict(X_test), y_test))\n",
    "\n",
    "# Print results sorted from best to least\n",
    "for i in np.argsort(scores)[::-1]:\n",
    "    print(\"Param settings:\", params[i])\n",
    "    print(\n",
    "        \"Iris dataset test accuracy (using confident learning):\\t\", \n",
    "        round(scores[i], 2),\n",
    "        \"\\n\"\n",
    "    )"
   ]
  }
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